Search

Summary: We propose a computationally efficient stochastic framework and an associated mathematical formulation to assess the improvement in the vibration response of wind turbines with active structural controllers. Uncertainties in the turbulent wind speed as well as in the structural parameters are considered in the formulation for analyzing the 2 important (rated and cut‐out) wind speed conditions. A multimodal wind turbine model based on a NREL 5‐MW wind turbine model is adopted for analysis, and an active tendon controller is used to develop the stochastic framework. Dynamic interaction between the rotor blades and the supporting tower, as well as the coupling induced by the pretwist of the blades, and a collective pitch controller are considered. A time‐evolving phase spectrum method together with a phase delay spectrum model is used to simulate the stochastic wind field, which is computationally less expensive. The probability density contours and the extreme value distributions of the blade tip displacements with and without the active controller are obtained by the wavelet‐based probability density evolution method, followed by the computation of the corresponding failure probabilities. It is observed that the improvement in reliability and performance of the rotor blades with the controller becomes statistically more effective as the wind speed increases (e.g., significant beneficial effects are noticed from the probability distributions in terms of the spread at the cut‐out wind speed). [ABSTRACT FROM AUTHOR]

The probability density evolution method (PDEM) provides a feasible approach for the dynamic response analysis of nonlinear stochastic structures. The key step in this regard is to solve a generalized density evolution equation (GDEE) in order to establish the probability density function (PDF). Previously, a finite difference method (FDM) has often been resorted to solve the GDEE. However, one may encounter the problem of mesh sensitivity in the application of FDM to the PDEM. To this end, a novel difference-wavelet method that can improve the finite difference result by means of a nonlinear wavelet density estimation method is proposed in the present paper. By exploiting the multi-resolution property of wavelet functions and by choosing the optimal scale at each instant, it is expected that the bothering mesh sensitivity issue in finite difference method can be overcome to some extent and a better probability density result can be obtained. In order to verify the proposed method, a single-degree-of-freedom (SDOF) oscillator and an 8-story frame structure are investigated in detail. The results show the notable superiority of the proposed method to finite difference method. [ABSTRACT FROM AUTHOR]

The key issue of the probability density evolution method (PDEM) is to solve a generalized density evolution equation (GDEE). Previously, the GDEE was solved in the framework of the point evolution method which is essentially a zero-order ensemble evolution method. In this paper, a first-order ensemble evolution method is proposed aiming at increasing the accuracy and robustness of the PDEM. The main idea of the proposed method is to incorporate information of standard deviation of each probability subdomain into the probability density evolution equation (PDEE) by introducing an ensemble velocity term. Compared with the point evolution method, the proposed method can truly reflect the fluctuation of a stochastic dynamic system. In order to estimate the ensemble velocity term accurately, a piecewise quadratic polynomial fitting method is also proposed. In addition, a GF-discrepancy based point selection method and a finite difference scheme that is total variation diminishing are adopted to solve the new PDEE. A single-degree-of-freedom oscillator, a Riccati equation and a 2-span 8-storey frame structure are investigated in detail to demonstrate the advantage of the proposed method over the original one. [ABSTRACT FROM AUTHOR]